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Bayesian Kernel Methods

Smola, Alexander; Schoelkopf, Bernhard

Description

Bayesian methods allow for a simple and intuitive representation of the function spaces used by kernel methods. This chapter describes the basic principles of Gaussian Processes, their implementation and their connection to other kernel-based Bayesian estimation methods, such as the Relevance Vector Machine.

CollectionsANU Research Publications
Date published: 2003
Type: Book chapter
URI: http://hdl.handle.net/1885/86069

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